With the recent increase of international exchange, use of machine translation which contributes to exchange between different languages is also increasing. In such machine translation, improving the accuracy of the machine translation is an important issue.
Two methods have mainly been used to improve the performance of the conventional machine translation system: one is to expand massive domain knowledge; and the other is to post-edit the result translated by a machine translation system.
The method to expand massive domain knowledge continuously expands the domain knowledge to be used in the machine translation system. In order to attain automatic translation of high quality in a specific domain, in particular, it is necessary not only to newly construct the knowledge that is appropriate to the domain but also to specialize the pre-constructed knowledge and the translation system to make them appropriate for the domain, for which specialized operations such as construction of coined words and patterns, engine error tuning, correction of pre-constructed knowledge and the like are required. These operations are carried out in general by trained linguists who are bilingual. This method is limited by the difficulty of making such bilinguists available as well as by the amount of time necessary to read a great amount of translated sentences Therefore, a great deal of time and cost are required to obtain a high quality translation in the specific domain, implying that the efficiency of translation is greatly reduced.
The post-editing method post-edits the result given by a machine translation system to overcome the shortcomings of the translation system. This method performs a statistical machine translation (hereinafter referred to as SMT) using a great number of parallel corpora, and the outcome of SMT is used to post edit the result translated by the conventional machine translation system.
The outcome of SMT is used not for improving the performance of a rule-based machine translation system but for obtaining more accurate translation by correcting the errors in the translated result. Hence, the corrected result does not alleviate the problems in the rule-based machine translation system. In addition, using such SMT result is not sufficient to resolve the internal problems that arise when the rule-based machine translation system is applied to a new domain.